{"cells":[{"cell_type":"code","execution_count":8,"metadata":{},"outputs":[],"source":["import math\n","import os\n","import datetime\n","import pydicom\n","import matplotlib.pyplot as plt\n","import numpy as np\n","\n","def gettimeformat(timevalue):\n","    return '%H%M%S.%f' if timevalue.find('.') > 0 else '%H%M%S'\n","\n","def getdatetime(datestr, timestr):\n","    date = datetime.datetime.strptime(datestr, '%Y%m%d')\n","    time = datetime.datetime.strptime(timestr, gettimeformat(timestr))\n","    return datetime.datetime.combine(date, time.time())\n","    \n","def calculatedecay(actualframeduration, framereferencetime, halflife, acquisitiondatetime):\n","    '''\n","    定义计算衰变开始时间\n","\n","    Parameters\n","    ----------\n","    actualframeduration : \n","        (0018,1242) IS Actual Frame Duration\n","    framereferencetime : \n","        (0054,1300) DS  Frame Reference Time\n","    halflife : \n","        (0018,1075) DS  Radionuclide Half Life\n","    acquisitiondatetime :\n","        (0008,0022) DA Acquisition Date\n","        (0008,0032) TM Acquisition Time\n","\n","    Returns\n","    -------\n","    scandatetime\n","        计算后的衰变开始时间，约为扫描开始时间\n","    '''\n","    frameduration =\tactualframeduration / 1000\n","    decayconstant = math.log(2) / halflife\n","    decayduringframe = decayconstant * frameduration\n","    averagecountratetimewithinframe = 1 / decayconstant * math.log(decayduringframe / (1-math.exp(-decayduringframe)))\n","    scandatetime = acquisitiondatetime - datetime.timedelta(seconds = framereferencetime / 1000) + datetime.timedelta(seconds = averagecountratetimewithinframe)\n","    return scandatetime\n",""]},{"cell_type":"markdown","metadata":{},"source":[" ** 取第一张图进行测试数据，进行计算验证：**\n"," ---\n","| Tag        | VR   |  Value | Description |\n","| --------   | :-----:  | :----  |  :----  |\n","| (0018,1242) | IS | 150000 | # 1, 6 Actual Frame Duration |\n","| (0054,1300) | DS | 74901.335499637 | # 1, 16 Frame Reference Time |\n","| (0018,1075) | DS | 6586.2 |  # 1, 6 Radionuclide Half Life |\n","| (0008,0022) | DA | 20170114 | # 1, 8 Acquisition Date |\n","| (0008,0032) | TM | 145019.000013 | # 1, 14 Acquisition Time |"]},{"cell_type":"code","execution_count":9,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"2017-01-14 14:50:19.000013\n"}],"source":["actualframeduration = 150000\n","framereferencetime = 74901.335499637\n","halflife = 6586.2\n","acqdt = getdatetime(\"20170114\", \"145019.000013\")\n","scandatetime = calculatedecay(actualframeduration, framereferencetime, halflife, acqdt)\n","print(scandatetime)"]},{"cell_type":"markdown","metadata":{},"source":[" ---\n"," ** 期望结果：**\n"," ---\n","| Tag        | VR   |  Value | Description |\n","| --------   | :-----:  | :----  | :----  |\n","| (0008,0021) | DA | 20170114 | # 1, 8 Series Date |\n","| (0008,0031) | TM | 145019.000000 | # 1, 14 Series Time |"]},{"cell_type":"markdown","metadata":{},"source":[" ---\n"," ** 读取整个PET序列并生成对比：**"]},{"cell_type":"code","execution_count":10,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"index:000,decaydt:2017-01-14 14:50:19.000013, seriesdt:2017-01-14 14:50:19 \nindex:030,decaydt:2017-01-14 14:50:19.000027, seriesdt:2017-01-14 14:50:19 \nindex:060,decaydt:2017-01-14 14:50:19.000030, seriesdt:2017-01-14 14:50:19 \nindex:090,decaydt:2017-01-14 14:50:19.000030, seriesdt:2017-01-14 14:50:19 \nindex:120,decaydt:2017-01-14 14:50:19.000032, seriesdt:2017-01-14 14:50:19 \nindex:150,decaydt:2017-01-14 14:50:19.000031, seriesdt:2017-01-14 14:50:19 \nindex:180,decaydt:2017-01-14 14:50:19.000024, seriesdt:2017-01-14 14:50:19 \n"}],"source":["def getdecaydatetime(ds):\n","    actualframeduration = float(ds[0x0018,0x1242].value)\n","    framereferencetime = float(ds[0x0054, 0x1300].value)\n","    halflife = float(ds[0x0054, 0x0016][0][0x0018, 0x1075].value)\n","    acqdt = getdatetime(ds[0x0008, 0x0022].value, ds[0x0008, 0x0032].value)\n","    return calculatedecay(actualframeduration, framereferencetime, halflife, acqdt)\n","\n","def getserialdatetime(ds):\n","    return getdatetime(ds[0x0008, 0x0021].value, ds[0x0008, 0x0031].value)\n","\n","def getacqdatetime(ds):\n","    return getdatetime(ds[0x0008, 0x0022].value, ds[0x0008, 0x0032].value)\n","\n","def getdatetimes(imagedir):\n","    datasets = tuple([pydicom.dcmread(os.path.join(imagedir, filename)) for filename in os.listdir(imagedir)])\n","    scandatetimes = tuple([getdecaydatetime(ds), getserialdatetime(ds), getacqdatetime(ds)] for ds in datasets)\n","\n","    count = 0\n","    for s, o, a in scandatetimes:\n","        if count % 30 == 0:\n","            print(\"index:{:0>3d},decaydt:{}, seriesdt:{} \".format(count, s, o, a))\n","            #print(\"index:{:0>3d},decaydt:{}, seriesdt:{},acq:{}\".format(count, s, o, a))\n","        count = count + 1\n","    return scandatetimes\n","\n","imagedir = \".\\\\pt\\\\siemens\"\n","scandatetimes = getdatetimes(imagedir)\n",""]},{"cell_type":"markdown","metadata":{},"source":[" ---\n"," 生成时间曲线："]},{"cell_type":"code","execution_count":11,"metadata":{},"outputs":[{"data":{"image/png":"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Axes>"},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["def showchart(scandatetimes):\n","    decaytime = [dt[0] for dt in scandatetimes]\n","    seriestime = [dt[1] for dt in scandatetimes]\n","    acqtime = [dt[2] for dt in scandatetimes]\n","    im = range(len(decaytime))\n","    plt.xlabel('Date Time')\n","    plt.ylabel('Image Index')\n","    plt.title('Calculate Decay Time')\n","    plt.plot(seriestime, im, 'green', label='Series DT')\n","    plt.plot(decaytime, im, 'blue', label='Decay DT')\n","    plt.plot(acqtime, im, 'red', label='Acquisition DT')\n","    plt.legend()\n","    plt.show()\n","\n","showchart(scandatetimes)"]},{"cell_type":"markdown","metadata":{},"source":[" ---\n"," 从曲线可以看出， 根据公式计算的衰变起始时间(蓝色)与序列时间(绿色)，基本一致，重叠在一起。采集时间是按照床位时间递进的。"]},{"cell_type":"markdown","metadata":{},"source":[" ---\n"," 下面是我们的数据在吉大一扫描模体的PET序列验证。"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"index:000,decaydt:2019-09-29 11:16:56.936853, seriesdt:2019-09-29 11:19:01 \nindex:030,decaydt:2019-09-29 11:16:56.936853, seriesdt:2019-09-29 11:19:01 \nindex:060,decaydt:2019-09-29 11:16:56.936853, seriesdt:2019-09-29 11:19:01 \n"},{"data":{"image/png":"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Axes>"},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["imagedir = \".\\\\pt\\\\IQModel\"\n","scandatetimes = getdatetimes(imagedir)\n","showchart(scandatetimes)\n",""]},{"cell_type":"markdown","metadata":{},"source":[" ---\n"," 下面是NueWise桂林临床的PET序列验证。"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":"index:000,decaydt:2019-11-14 12:02:24.901333, seriesdt:2019-11-14 11:57:07 \nindex:030,decaydt:2019-11-14 12:02:24.901333, seriesdt:2019-11-14 11:57:07 \nindex:060,decaydt:2019-11-14 12:02:25.121333, seriesdt:2019-11-14 11:57:07 \nindex:090,decaydt:2019-11-14 12:03:44.011333, seriesdt:2019-11-14 11:57:07 \nindex:120,decaydt:2019-11-14 12:03:43.270333, seriesdt:2019-11-14 11:57:07 \nindex:150,decaydt:2019-11-14 12:02:25.418333, seriesdt:2019-11-14 11:57:07 \nindex:180,decaydt:2019-11-14 12:03:42.113333, seriesdt:2019-11-14 11:57:07 \nindex:210,decaydt:2019-11-14 12:02:24.807333, 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\n"},{"data":{"image/png":"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